Word2Vec

```
public class Word2Vec
extends java.lang.Object
```
Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located close to one another in the space. Word2vec can utilize either of two model architectures to produce a distributed representation of words: continuous bag-of-words (CBOW) or continuous skip-gram. In the continuous bag-of-words architecture, the model predicts the current word from a window of surrounding context words. The order of context words does not influence prediction (bag-of-words assumption). In the continuous skip-gram architecture, the model uses the current word to predict the surrounding window of context words. The skip-gram architecture weighs nearby context words more heavily than more distant context words. According to the authors' note, CBOW is faster while skip-gram is slower but does a better job for infrequent words.

Fields
Modifier and Type	Field and Description
`smile.data.DataFrame`	`vectors` The vector space.
`java.lang.String[]`	`words` The vocabulary.

Constructors
Constructor and Description
`Word2Vec(java.lang.String[] words, float[][] vectors)` Constructor.

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`float[]`	`apply(java.lang.String word)` Returns the vector embedding of a word.
`int`	`dimension()` Returns the dimension of vector space.
`float[]`	`get(java.lang.String word)` Returns the vector embedding of a word.
`static Word2Vec`	`of(java.nio.file.Path file)` Loads a word2vec model from binary file of ByteOrder.LITTLE_ENDIAN.
`static Word2Vec`	`of(java.nio.file.Path file, java.nio.ByteOrder order)` Loads a word2vec model from binary file.
`static Word2Vec`	`text(java.nio.file.Path file)` Loads a GloVe model from text file.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

public final smile.data.DataFrame vectors

The vector space.

public float[] get(java.lang.String word)

Returns the vector embedding of a word.

apply
```
public float[] apply(java.lang.String word)
```
Returns the vector embedding of a word. For Scala convenience.

public static Word2Vec of(java.nio.file.Path file)
                   throws java.io.IOException

Loads a word2vec model from binary file of ByteOrder.LITTLE_ENDIAN.

public static Word2Vec of(java.nio.file.Path file,
                          java.nio.ByteOrder order)
                   throws java.io.IOException

Loads a word2vec model from binary file.

public static Word2Vec text(java.nio.file.Path file)
                     throws java.io.IOException

Loads a GloVe model from text file.

Class Word2Vec